Psychosocial factors influencing aggressive driving among commercial and private automobile drivers in Lagos metropolis

Balogun, Shyngle K.; Shenge, Nyitor Alexander; Oladipo, Samuel E.

doi:10.1016/j.soscij.2011.07.004

Cited by 24 publications

(16 citation statements)

References 15 publications

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“…Drivers are humans, and human behavior has multiple influencing factors that are difficult to consider such as emotions, personality, medical conditions, hunger or thirst [1]. These influencing factors can cause a driver to differently behave when the same situation repeats.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Segmentation and Clustering of Car-Following Behavior: Recognition of Driving Patterns

Higgs

Abbas

2015

IEEE Trans. Intell. Transport. Syst.

124

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Driving behavior can be influenced by many factors that are not feasible to collect in driving behavior studies. The research presented in this paper investigates the characteristics of a wide range of driving behaviors linking driving states to the drivers' actions. The proposed methodology is structured such that a known state can be linked to multiple actions, thus accounting for the effects of unknown driving state factors. A two-step algorithm was developed and used for the segmentation and clustering of driving behaviors. The algorithm segments and clusters car-following behaviors based on eight state-action variables: the longitudinal acceleration, the lateral acceleration, the yaw rate, the vehicle speed, the lane offset, the yaw angle, the range, and the range rate. The results of this methodology are state-action clusters that define the driving pattern of drivers. The sample used in this paper included 20 different drivers, i.e., ten car and ten truck drivers. The results revealed that behavior patterns were different between car drivers but not between truck drivers. The results also show that car drivers exhibit behaviors that are unique to each driver, whereas truck drivers show a common driving pattern. The characteristics and frequency of recognized driving patterns are provided in this paper, along with the corresponding modeling parameters of each pattern.Index Terms-Clustering methods, data mining, driver behavior, traffic flow theory.

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Section: Discussionmentioning

confidence: 99%

“…Typically, driver behavior studies collect data that encompass both states and actions and attempt to develop a more accurate mapping between them. These efforts do not usually include many influencing factors of human behavior, such as emotion, personality, hunger, or thirst [1].…”

Section: Introductionmentioning

confidence: 99%

Segmentation and Clustering of Car-Following Behavior: Recognition of Driving Patterns

Higgs

Abbas

2015

IEEE Trans. Intell. Transport. Syst.

124

View full text Add to dashboard Cite

show abstract

“…For a sluggish driver, b should be large. Conversely, excited or aggressive drivers such as commercial or teenage drivers will have a small reaction time and corresponding small transition distance [18], so b should be small.…”

Section: Traffic Flow Modelsmentioning

confidence: 99%

“…The PW model is based on the assumption that vehicles on a road have similar behavior. Smooth traffic velocities and density distributions are employed [17], and alignment (harmonization) occurs with a constant velocity [18]. Unfortunately, this results in unrealistic velocity and density behavior [19].…”

Section: Introductionmentioning

confidence: 99%

A macroscopic traffic model for traffic flow harmonization

Khan

Gulliver

2018

Eur. Transp. Res. Rev.

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Traffic flow will harmonize to forward conditions. The time and distance required for harmonization can have a significant effect on the traffic density behavior. The flow can evolve into clusters of vehicles or become uniform depending on parameters such as safe time headway and safe distance headway. In this paper, a new model is presented to provide a realistic characterization of traffic behavior during the harmonization period. Results are presented for a discontinuous density distribution on a circular road which shows that this model produces more realistic traffic behavior than other models in the literature.

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“…Because anger reactions are learned in the course of life, these harmful behaviors can be replaced by good behaviors (Deffenbacher & Stark, 1992). Indeed, some researchers have explored the effect of cognitive-behavioral treatment on high-anger drivers (Balogun, Shenge, & Oladipo, 2012;Del Vecchio & O'Leary, 2004). Thus, the Chinese version of the DAX may be a useful tool for developing personalized anger management education and training classes based on various expressions of driving anger.…”

Section: Tablementioning

confidence: 99%

Psychometric adaptation of the driving anger expression inventory in a Chinese sample

Zhang

et al. 2015

Transportation Research Part F: Traffic Psychology and Behaviou

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Psychosocial factors influencing aggressive driving among commercial and private automobile drivers in Lagos metropolis

Cited by 24 publications

References 15 publications

Segmentation and Clustering of Car-Following Behavior: Recognition of Driving Patterns

Segmentation and Clustering of Car-Following Behavior: Recognition of Driving Patterns

A macroscopic traffic model for traffic flow harmonization

Psychometric adaptation of the driving anger expression inventory in a Chinese sample

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